Quantitative structure-activity relationships for cycloguanil analogs as PfDHFR inhibitors using mathematical molecular descriptors

Subhash C Basak, D. Mills

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Computed molecular descriptors were used to develop quantitative structure-activity relationships (QSARs) for binding affinities (Ki) for a set of 58 cycloguanil (2,4-diamino-1,6-dihydro-1,3,5-triazine) analogues for dihydrofolate reductase (DHFR) enzyme extracted from wild and A16V+S108T mutant type (a double mutation) malaria parasite Plasmodium falciparum (Pf). High-quality models were obtained in both cases. The results of statistical analyses show that ridge regression (RR) outperformed the two other modelling methods, principal component regression (PCR) and partial least squares (PLS). For both enzymes, recognition of the inhibitors was based on four broad categories of descriptors encoding information on: (1) the electronic character of the various atoms in the molecule, (2) the size and shape of the structure, (3) the degree of branching in the molecular skeleton, and (4) two to five atom molecular fragments with aliphatic carbon at one end and aliphatic or aromatic carbon or nitrogen at the other end. The subsets of influential descriptors underlying the QSARs for the wild versus the mutant DHFR are quite non-overlapping. This indicates that the two enzymes recognize the inhibitor molecules on the basis of mutually distinct structural attributes. Such differential QSARs can be useful in the design of novel drugs active against malaria parasites which are growing in resistant to existing chemotherapeutic agents.

Original languageEnglish (US)
Pages (from-to)215-229
Number of pages15
JournalSAR and QSAR in environmental research
Volume21
Issue number3
DOIs
StatePublished - Apr 2010

Keywords

  • Cycloguanil analogues
  • Differential QSARs
  • Dihydrofolate reductase (DHFR) inhibitors
  • High-dimensional pharmacophore
  • Plasmodium falciparum (Pf)
  • Ridge regression

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